Probability Theory¶
Jaynes, E. T. (2003). Probability Theory: The Logic of Science. Cambridge University Press.
Cited by¶
6 citations across 6 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Bayesian Updating
This sourceFoundational Bayesian epistemology: argues that the only access to a system's true state is through inferential reasoning over noisy data conditioned on a model of the noise — formalizing the epistemological asymmetry between observation and reality.
- Epistemic Humility
- Epistemic humility encourages thinking in terms of epistemic position: Who knows what, with what warrant, and why?—the Bayesian framing of degrees of belief and conditional updating Jaynes (2003) develops as the logic of probability.
This sourceTreats probability as extended logic: degrees of belief updated by conditioning (Bayesian inference) as the consistent logic of plausible reasoning. SUPPORTS marker 221 (Bayesian framing of degrees of belief and conditional updating as the logic of probability).
Supported in partVerified against the publisher's abstract
The abstract backs the 'probability as logic' clause for Jaynes but says nothing of degrees of belief, conditional updating, or epistemic humility.
“The standard rules of probability can be interpreted as uniquely valid principles in logic. In this book, E. T. Jaynes dispels the imaginary distinction between 'probability theory' and 'statistical inference', leaving a logical unity and simplicity, which provides greater technical power and flexibility in applications.”
- Epistemic humility encourages thinking in terms of epistemic position: Who knows what, with what warrant, and why?—the Bayesian framing of degrees of belief and conditional updating Jaynes (2003) develops as the logic of probability.
- Measurement Uncertainty and Observational Noise
- This creates epistemological asymmetry: we can never measure the true state, only infer it from noisy observations paired with a model of the noise, an inferential stance Jaynes (2003) develops in his treatment of probability as the logic of science.
This sourceFoundational Bayesian epistemology: argues that the only access to a system's true state is through inferential reasoning over noisy data conditioned on a model of the noise — formalizing the epistemological asymmetry between observation and reality.
Supported in partVerified against the publisher's abstract
Confirms Jaynes frames probability as the logic of science and inference from incomplete information; the abstract does not state the claim's 'never measure the true state / noise model' asymmetry clause.
“The standard rules of probability can be interpreted as uniquely valid principles in logic.”
- This creates epistemological asymmetry: we can never measure the true state, only infer it from noisy observations paired with a model of the noise, an inferential stance Jaynes (2003) develops in his treatment of probability as the logic of science.
- Stochasticity vs. Determinism
- In software testing, deterministic systems are debuggable (reproduce bugs reliably by setting initial conditions), whereas stochastic systems require statistical testing, Monte Carlo methods, and ensemble verification—a transfer Jaynes (2003) develops by treating probability as an extension of logic across domains.
This sourceFoundational Bayesian epistemology: argues that the only access to a system's true state is through inferential reasoning over noisy data conditioned on a model of the noise — formalizing the epistemological asymmetry between observation and reality.
Supported in partVerified against the publisher's abstract
Jaynes's abstract states probability theory as uniquely valid principles in logic applied across domains, but says nothing about software testing, debuggability, Monte Carlo methods, or ensemble verification.
“The standard rules of probability can be interpreted as uniquely valid principles in logic. In this book, E. T. Jaynes dispels the imaginary distinction between 'probability theory' and 'statistical inference', leaving a logical unity and simplicity, which provides greater technical power and flexibility in applications.”
- In software testing, deterministic systems are debuggable (reproduce bugs reliably by setting initial conditions), whereas stochastic systems require statistical testing, Monte Carlo methods, and ensemble verification—a transfer Jaynes (2003) develops by treating probability as an extension of logic across domains.
- Symmetric Response to Asymmetric State
- Bayesian inference. A uniform prior is not informationally neutral; over hypotheses whose prior evidence is asymmetric it pulls posteriors toward the under-evidenced hypothesis.
This sourceA uniform prior is not informationally neutral; over an evidentially asymmetric hypothesis space it allocates posterior mass toward the under-evidenced hypothesis, and stronger evidence is required to drive the posterior to where an evidence-weighted prior already sat.
- Bayesian inference. A uniform prior is not informationally neutral; over hypotheses whose prior evidence is asymmetric it pulls posteriors toward the under-evidenced hypothesis.
Domain-specific¶
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Read against the text for 3 of 6 citations: 3 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
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Links previously used in the corpus¶
Before the registry existed this work was also linked 1 other way.
Registry ID ref:c40e923f95c0 · see in the full table